From science-superpowers
Surveys established methods, known confounds, and prior effect sizes before designing an analysis. Use after framing a question to ground methods and avoid rediscovering known artifacts.
How this skill is triggered — by the user, by Claude, or both
Slash command
/science-superpowers:surveying-prior-workThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Before designing an analysis, ground the question and your chosen methods in what is already known. Most questions have established methods, known confounds, and prior effect sizes. Reinventing a method badly — or rediscovering a known artifact and reporting it as a finding — wastes effort and erodes credibility.
Before designing an analysis, ground the question and your chosen methods in what is already known. Most questions have established methods, known confounds, and prior effect sizes. Reinventing a method badly — or rediscovering a known artifact and reporting it as a finding — wastes effort and erodes credibility.
Core principle: Find out what is already known before you generate new claims.
This is the science analog of reading the existing codebase before writing new code. It is a flexible skill — adapt depth to the stakes of the investigation.
framing-research-questions, before designing-the-analysisSurvey four things:
science-superpowers:dispatching-parallel-investigations for the workflow. This keeps your own context clean.After grounding, invoke science-superpowers:designing-the-analysis. Bring forward the adopted methods, the confound list, and the prior effect size for powering the design.
npx claudepluginhub wangdepin/science-superpowers --plugin science-superpowers3plugins reuse this skill
First indexed Jun 16, 2026
Surveys established methods, known confounds, and prior effect sizes before designing an analysis. Use after framing a question to ground methods and avoid rediscovering known artifacts.
Statistical method selection, guidance, and results reporting. Triggers when user says: 'which statistical test', 'analyze data', 'statistical analysis', 'p-value', 'significance test', 'power analysis', 'sample size calculation', 'effect size', 'regression', 'ANOVA', 'compare groups', 'correlation analysis', 'assumption check', 'meta-analysis', 'pool effect sizes', 'pooled effect', 'forest plot', 'funnel plot', 'heterogeneity', 'random-effects model', 'I-squared'. Guides users through choosing the right statistical test, checking assumptions, generating implementation code, reporting results in APA format, and running the meta-analysis synthesis step of a systematic review. Use this skill whenever the user needs help with quantitative data analysis or pooling effect sizes across studies.
Matches research questions to appropriate designs, sampling strategies, and validity controls, and reframes stuck problems with cross-domain analogies and first-principles deconstruction.